IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4441-d1160459.html
   My bibliography  Save this article

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Author

Listed:
  • Marcin Kaminski

    (Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland)

  • Tomasz Tarczewski

    (Department of Automatics and Measurement Systems, Nicolaus Copernicus University, 87-100 Toruń, Poland)

Abstract

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

Suggested Citation

  • Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4441-:d:1160459
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4441/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/11/4441/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dorian Skrobek & Jaroslaw Krzywanski & Marcin Sosnowski & Ghulam Moeen Uddin & Waqar Muhammad Ashraf & Karolina Grabowska & Anna Zylka & Anna Kulakowska & Wojciech Nowak, 2023. "Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives," Energies, MDPI, vol. 16(8), pages 1-12, April.
    2. Stanisław Duer & Jan Valicek & Jacek Paś & Marek Stawowy & Dariusz Bernatowicz & Radosław Duer & Marcin Walczak, 2021. "Neural Networks in the Diagnostics Process of Low-Power Solar Plant Devices," Energies, MDPI, vol. 14(9), pages 1-18, May.
    3. Marcin Kaminski, 2020. "Nature-Inspired Algorithm Implemented for Stable Radial Basis Function Neural Controller of Electric Drive with Induction Motor," Energies, MDPI, vol. 13(24), pages 1-25, December.
    4. Jose A. Ruz-Hernandez & Larbi Djilali & Mario Antonio Ruz Canul & Moussa Boukhnifer & Edgar N. Sanchez, 2022. "Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles," Energies, MDPI, vol. 15(23), pages 1-19, November.
    5. Youguang Guo & Xin Ba & Lin Liu & Haiyan Lu & Gang Lei & Wenliang Yin & Jianguo Zhu, 2023. "A Review of Electric Motors with Soft Magnetic Composite Cores for Electric Drives," Energies, MDPI, vol. 16(4), pages 1-17, February.
    6. Krzysztof Szabat & Karol Wróbel & Krzysztof Dróżdż & Dariusz Janiszewski & Tomasz Pajchrowski & Adrian Wójcik, 2020. "A Fuzzy Unscented Kalman Filter in the Adaptive Control System of a Drive System with a Flexible Joint," Energies, MDPI, vol. 13(8), pages 1-18, April.
    7. Ahmed G. Mahmoud A. Aziz & Almoataz Y. Abdelaziz & Ziad M. Ali & Ahmed A. Zaki Diab, 2023. "A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques," Energies, MDPI, vol. 16(6), pages 1-32, March.
    8. Toomas Vaimann & Ants Kallaste, 2023. "Additive Manufacturing of Electrical Machines—Towards the Industrial Use of a Novel Technology," Energies, MDPI, vol. 16(1), pages 1-10, January.
    9. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    10. Lelisa Wogi & Tadele Ayana & Marcin Morawiec & Andrzej Jąderko, 2022. "A Comparative Study of Fuzzy SMC with Adaptive Fuzzy PID for Sensorless Speed Control of Six-Phase Induction Motor," Energies, MDPI, vol. 15(21), pages 1-29, November.
    11. Piotr Derugo & Krzysztof Szabat & Tomasz Pajchrowski & Krzysztof Zawirski, 2022. "Fuzzy Adaptive Type II Controller for Two-Mass System," Energies, MDPI, vol. 15(2), pages 1-24, January.
    12. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    13. Max A. Buettner & Niklas Monzen & Christoph M. Hackl, 2022. "Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art," Energies, MDPI, vol. 15(5), pages 1-38, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Kaczmarczyk & Radoslaw Stanislawski & Jaroslaw Szrek & Marcin Kaminski, 2024. "Adaptive Sliding Mode Control Based on a Radial Neural Model Applied for an Electric Drive with an Elastic Shaft," Energies, MDPI, vol. 17(4), pages 1-28, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
    2. Jacek Kabziński & Przemysław Mosiołek, 2022. "Observer-Based, Robust Position Tracking in Two-Mass Drive System," Energies, MDPI, vol. 15(23), pages 1-28, November.
    3. Marwa Ben Slimene & Mohamed Arbi Khlifi, 2022. "Investigation on the Effects of Magnetic Saturation in Six-Phase Induction Machines with and without Cross Saturation of the Main Flux Path," Energies, MDPI, vol. 15(24), pages 1-18, December.
    4. Karol Wróbel & Kacper Śleszycki & Amanuel Haftu Kahsay & Krzysztof Szabat & Seiichiro Katsura, 2023. "Robust Speed Control of Uncertain Two-Mass System," Energies, MDPI, vol. 16(17), pages 1-17, August.
    5. Anto Anbarasu Yesudhas & Young Hoon Joo & Seong Ryong Lee, 2022. "Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed," Energies, MDPI, vol. 15(9), pages 1-17, April.
    6. Jacek Paś, 2023. "Issues Related to Power Supply Reliability in Integrated Electronic Security Systems Operated in Buildings and Vast Areas," Energies, MDPI, vol. 16(8), pages 1-22, April.
    7. Xiaoyu Deng & Ruo Mo & Pengliang Wang & Junru Chen & Dongliang Nan & Muyang Liu, 2023. "Review of RoCoF Estimation Techniques for Low-Inertia Power Systems," Energies, MDPI, vol. 16(9), pages 1-19, April.
    8. Ghoroghi, Ali & Petri, Ioan & Rezgui, Yacine & Alzahrani, Ateyah, 2023. "A deep learning approach to predict and optimise energy in fish processing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    9. Dominik Łuczak, 2021. "Nonlinear Identification with Constraints in Frequency Domain of Electric Direct Drive with Multi-Resonant Mechanical Part," Energies, MDPI, vol. 14(21), pages 1-12, November.
    10. Humam Al-Baidhani & Abdullah Sahib & Marian K. Kazimierczuk, 2023. "State Feedback with Integral Control Circuit Design of DC-DC Buck-Boost Converter," Mathematics, MDPI, vol. 11(9), pages 1-18, May.
    11. Jacek Kabziński & Przemysław Mosiołek, 2021. "Integrated, Multi-Approach, Adaptive Control of Two-Mass Drive with Nonlinear Damping and Stiffness," Energies, MDPI, vol. 14(17), pages 1-23, September.
    12. Wentao Li & Qiankun Liu & Siyang Ye & Surong Huang, 2024. "Optimization of Stator Structure for Improved Accuracy in Variable Reluctance Resolvers Using Advanced Machine Learning Techniques," Energies, MDPI, vol. 17(21), pages 1-30, October.
    13. Víctor Ballestín-Bernad & Jesús Sergio Artal-Sevil & José Antonio Domínguez-Navarro, 2023. "Prototype of a Two-Phase Axial-Gap Transverse Flux Generator Based on Reused Components and 3D Printing," Energies, MDPI, vol. 16(4), pages 1-20, February.
    14. Youguang Guo & Yunfei Yu & Haiyan Lu & Gang Lei & Jianguo Zhu, 2024. "Enhancing Performance of Permanent Magnet Motor Drives through Equivalent Circuit Models Considering Core Loss," Energies, MDPI, vol. 17(8), pages 1-17, April.
    15. Francesco Lucchini & Riccardo Torchio & Nicola Bianchi, 2024. "A Survey on the Sustainability of Traditional and Emerging Materials for Next-Generation EV Motors," Energies, MDPI, vol. 17(23), pages 1-19, November.
    16. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    17. Mateusz Malarczyk & Mateusz Zychlewicz & Radoslaw Stanislawski & Marcin Kaminski, 2023. "Electric Drive with an Adaptive Controller and Wireless Communication System," Future Internet, MDPI, vol. 15(2), pages 1-20, January.
    18. Kijo-Kleczkowska, Agnieszka & Gnatowski, Adam & Krzywanski, Jaroslaw & Gajek, Marcin & Szumera, Magdalena & Tora, Barbara & Kogut, Krzysztof & Knaś, Krzysztof, 2024. "Experimental research and prediction of heat generation during plastics, coal and biomass waste combustion using thermal analysis methods," Energy, Elsevier, vol. 290(C).
    19. Zheng Xu, 2022. "Three Technical Challenges Faced by Power Systems in Transition," Energies, MDPI, vol. 15(12), pages 1-21, June.
    20. Karol Wróbel & Kacper Śleszycki & Krzysztof Szabat & Seiichiro Katsura, 2021. "Application of Multilayer Observer for a Drive System with Flexibility," Energies, MDPI, vol. 14(24), pages 1-19, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4441-:d:1160459. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.